Current practice of emergency radiology in Turkey and future expectations: a survey study

PURPOSE

The development of emergency radiology (ER) in Turkey has accelerated with the increase in the number of patients admitted to emergency departments. We aimed to present and discuss the responses to a survey distributed to radiologists in Turkey, which included questions about the current practice of ER and future expectations.

METHODS

A survey with 29 questions enquiring about the infrastructure of respondents' hospitals and radiology units, information about emergency services and ER (including patient volume), the number of staff and equipment, the ER working plan and reporting method, and training in the field of ER were distributed to members of the Turkish Radiological Society by email.

RESULTS

The response rate was 21.97% (328/1.493). The presence of distinct ER units in radiology departments was confirmed by 40.55% of respondents, while for 34.25%, ER was located inside the emergency room. Of the respondents, 26.96% stated they believed that emergency cases should be reported by emergency radiologists, and the necessity for an ER subunit in the emergency room was agreed upon by 58.64% of contributors. The majority of respondents (69.54%) agreed with the opinion that residents should receive their ER training in an ER unit.

CONCLUSION

Keeping abreast of current ER practices and radiologists' expectations may be helpful for improving national ER practices and academic studies.

Results of the 2020 Survey of the American Alliance of Academic Chief Residents in Radiology

RATIONALE AND OBJECTIVES

An annual survey of chief residents in accredited North American radiology programs is conducted by the American Alliance of Academic Chief Residents in Radiology (A³CR²). The purpose of this study is to summarize the 2020 A³CR² chief resident survey.

MATERIALS AND METHODS

An online survey was distributed to chief residents from 194 Accreditation Council on Graduate Medical Education-accredited radiology residencies. Questions were designed to gather information about residency program practices, benefits, fellowship or advanced interventional radiology (IR) training choices, and the integration of IR training. Subsets of questions focused on the perception of corporatization, non-physician providers (NPPs), and artificial intelligence (AI) in radiology and their relationship to the radiology job market.

RESULTS

174 individual responses from 94 programs were provided, yielding a 48 % program response rate. Extended emergency department coverage has steadily decreased over the last 5 years (2016-2020), however only 52 % of programs have independent overnight call (without attending coverage). Regarding the impact of new integrated IR residencies on training, 42 % indicated there was no appreciable impact on their DR or IR training, while 20 % indicated DR training for IR residents suffered and 19 % indicated IR training for DR residents suffered. Corporatization in radiology was perceived as the biggest potential threat to the future job market.

CONCLUSIONS

Integration of IR residency did not detrimentally affect DR or IR training in most programs. Radiology resident perception of corporatization, NPPs, and AI may help residency programs shape educational content.

Indexing Imaging Reports for Data Sharing: A Study of Mapping Using RadLex Playbook and LOINC

New use cases and the need for quality control and imaging data sharing in health studies require the capacity to align them to reference terminologies. We are interested in mapping the local terminology used at our center to describe imaging procedures to reference terminologies for imaging procedures (RadLex Playbook and LOINC/RSNA Radiology Playbook). We performed a manual mapping of the 200 most frequent imaging report titles at our center (i.e. 73.2% of all imaging exams). The mapping method was based only on information explicitly stated in the titles. The results showed 57.5% and 68.8% of exact mapping to the RadLex and LOINC/RSNA Radiology Playbooks, respectively. We identified the reasons for the mapping failure and analyzed the issues encountered.

Change and consistency in Acta Radiologica over 100 years

Acta Radiologica celebrates its 100th anniversary in 2021. In this article, the foundation of the journal and its editors are described. During 100 years, the manuscript structure changed from single-author verbose monographs to multi-author collaborations on statistically analyzed research subjects. The authorship changed from purely Nordic authors to a truly international cadre of authors, and the size of the journal increased considerably, in issues per year, printed pages, and published articles per year. The Foundation of Acta Radiologica has been able to give out two prizes, the Xenia Forsselliana and the Acta Radiologica International Scientific Prize for the best manuscripts each year. The increasing submissions of manuscripts is an indication that Acta Radiologica will continue to publish important scientific results for many years to come.

Radiomics to better characterize small renal masses

PURPOSE

Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies.

METHODS

A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness.

CONCLUSION

Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.

Appropriate use criteria for cardiovascular MRI: SIC - SIRM position paper Part 2 (myocarditis, pericardial disease, cardiomyopathies and valvular heart disease)

Cardiovascular magnetic resonance (CMR) has emerged as an accurate diagnostic technique for the evaluation of patients with cardiac disease in the majority of clinical settings, thanks to an established additional diagnostic and prognostic value. This document has been developed by a joined group of experts of the Italian Society of Cardiology (SIC) and Italian Society of Radiology (SIRM) to provide a summary about the current state of technology and clinical applications of CMR, to improve the clinical diagnostic pathways and to promote its inclusion in clinical practice. The writing committee consisted of members and experts of both societies in order to develop a more integrated approach in the field of cardiac imaging. This section 2 will cover myocarditis, pericardial disease, cardiomyopathies and valvular heart disease.

Evolving Use of fMRI in Medicare Beneficiaries

Using the Medicare Physician-Supplier Procedure Summary Master File, we evaluated the evolving use of fMRI in Medicare fee-for-service beneficiaries from 2007 through 2017. Annual use rates (per 1,000,000 enrollees) increased from 17.7 to 32.8 through 2014 and have remained static since. Radiologists have remained the dominant specialty group from 2007 to 2017 (86.4% and 88.6% of all services, respectively), and the outpatient setting has remained the dominant place of service (65.4% and 65.4%, respectively).

The Impact of COVID-19 on the Use of Radiology Resources in a Tertiary Hospital

BACKGROUND

The coronavirus disease 2019 (COVID-19) has escalated to be a global threat to public health. Analysis of the use of radiology resources may render us insight regarding the public health behavior during pandemic. We measured the influence COVID-19 had on the use of radiology resources in terms of the number of examinations performed, and turnaround time for portable radiography.

METHODS

This study was conducted at a tertiary hospital located in area where the prevalence of COVID-19 infection was low (0.01%). We compared the number of radiology examinations 1) before pandemic (in 2019) vs. during peak of pandemic (January to March 2020), and 2) before pandemic vs. after the peak of pandemic (April to June 2020) via t-tests. We repeated similar analyses for subgroups as follows: gender, age, department (outpatient, inpatient, emergency, screening), body parts, and modality. We also performed a survey of radiologic technologists regarding the turnaround time and rate-limiting step of portable radiography for patients with and without suspicion or confirmation of COVID-19.

RESULTS

Although not statistically significant, the daily number of examinations during the peak of pandemic decreased by 9 percentage points (2,638 vs. 2,413; difference [95% CI], -225 [-489, 38]; P = 0.094). The percentage change was especially notable for children, emergency, and screening department (25, 19, and 44 percentage points, respectively). After the peak of the pandemic, the number of examinations increased back to near the pre-pandemic level (2,638 vs. 2,588; -50 [-317, 218]; P = 0.71). The turnaround time for portable radiography tended to be longer for patients with suspicion or confirmation of COVID-19, with donning personal protective equipment being the major rate-limiting step.

CONCLUSION

The number of examinations decreased during the pandemic, reflecting the tendency of the public to refrain from seeking medical care even in a community of low infection risk. Nevertheless, burden of healthcare providers may not have decreased as much, considering longer turnaround time required for COVID-19 related examinations.

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers' and radiologists' experience.

Artificial Intelligence in Neuroradiology: Current Status and Future Directions

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

Radiological approach to COVID-19 pneumonia with an emphasis on chest CT

Coronavirus disease 2019 (COVID-19) has recently become a worldwide outbreak with several millions of people infected and more than 160.000 deaths. A fast and accurate diagnosis in this outbreak is critical to isolate and treat patients. Radiology plays an important role in the diagnosis and management of the patients. Among various imaging modalities, chest CT has received attention with its higher sensitivity and specificity rates. Shortcomings of the real-time reverse transcriptase-polymerase chain reaction test, including inappropriate sample collection and analysis methods, initial false negative results, and limited availability has led to widespread use of chest CT in the diagnostic algorithm. This review summarizes the role of radiology in COVID-19 pneumonia, diagnostic accuracy of imaging, and chest CT findings of the disease.

Analysis of magnetic resonance imaging features of ovarian thecoma

To investigate the magnetic resonance imaging (MRI) findings in ovarian thecoma and improve preoperative diagnostic accuracy.Retrospective analysis was performed on 45 patients with surgically and pathologically confirmed ovarian thecoma. Patients were grouped into those with maximum lesion diameter ≥5 cm and <5 cm. Diagnostic scores (up to 6 points) were evaluated on the basis of MRI performance.The ≥5 cm group contained 36 cases (cystic necrosis, 32 cases) with the following findings: T1WI: isointense signal, 22 cases; slightly hypointense signal, 14 cases; T2WI: isointense signal, 6 cases; slightly hypointense signal, 21 cases; slightly hyperintense signal, 9 cases; Diffusion-weighted imaging (DWI): hyperintense signal, 23 cases; mixed hyperintense signal, 13 cases; slight enhancement on dynamic enhanced scans; pelvic fluid accumulation, 31 cases. The diagnostic score evaluations yielded 6 points in 31 cases, 5 points in 1 case, 4 points in 2 cases, and 3 points in 2 cases. The <5 cm group contained 9 cases (cystic necrosis, 3 cases) with the following findings: T1WI: isointense signal, 3 cases; slightly hypointense signal, 6 cases; T2WI: isointense signal, 2 cases; slightly hypointense signal, 4 cases; slightly hyperintense signal, 3 cases; DWI, hyperintense signal; slight enhancement in 8 cases and significant enhancement in 1 case; pelvic fluid accumulation, 4 cases. The diagnostic score evaluations yielded 6 points in 3 cases, 5 points in 1 case, 4 points in 4 cases, and 3 points in 1 case. (iii) Incidence of pelvic fluid accumulation and cystic necrosis differed depending on the size of the lesion (P = .007, .000).Larger lesions show hyperintense or mixed hyperintense signals on DWI along with pelvic fluid and cystic necrosis; whereas, smaller lesions show a hyperintense signal on DWI, cystic necrosis is rare. MRI characteristics along with the patient age and laboratory findings can improve the accuracy of preoperative diagnosis of these lesions.

Artificial intelligence in clinical imaging: a health system approach

The development and application of artificial intelligence (AI) to radiology requires an approach that encompasses a health system. The UK government and National Health Service (NHS) are creating an ecosystem to facilitate academic/industrial partnerships aimed at accelerating the creation of relevant and robust AI tools, which will improve the development and delivery of healthcare imaging. A series of recent initiatives are described, which will drive the development and adoption of AI in clinical imaging.

Open access image repositories: high-quality data to enable machine learning research

Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.

Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging

Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment.

Radiogenomics in renal cell carcinoma

Radiogenomics, a field of radiology investigating the association between the imaging features of a disease and its gene expression pattern, has expanded considerably in the last few years. Recent advances in whole-genome sequencing of clear cell renal cell carcinoma (ccRCC) and the identification of mutations with prognostic significance have led to increased interest in the relationship between imaging and genomic data. ccRCC is particularly suitable for radiogenomic analysis as the relative paucity of mutated genes allows for more straightforward genomic-imaging associations. The ultimate aim of radiogenomics of ccRCC is to retrieve additional data for accurate diagnosis, prognostic stratification, and optimization of therapy. In this review article, we will present the state-of-the-art of radiogenomics of ccRCC, and after briefly reviewing updates in genomics, we will discuss imaging-genomic associations for diagnosis and staging, prognosis, and for assessment of optimal therapy in ccRCC.

Perceptual and Interpretive Error in Diagnostic Radiology-Causes and Potential Solutions

Interpretation of increasingly complex imaging studies involves multiple intricate tasks requiring visual evaluation, cognitive processing, and decision-making. At each stage of this process, there are opportunities for error due to human factors including perceptual and ergonomic conditions. Investigation into the root causes of interpretive error in radiology first began over a century ago. In more recent work, there has been increasing recognition of the limits of human image perception and other human factors and greater acknowledgement of the role of the radiologist's environment in increasing the risk of error. This article reviews the state of research on perceptual and interpretive error in radiology. This article focuses on avenues for further error examination, and strategies for mitigating these errors are discussed. The relationship between artificial intelligence and interpretive error is also considered.

The need for standardization of nuclear cardiology reporting and data system (NCAD-RADS): Learning from coronary artery disease (CAD), breast imaging (BI), liver imaging (LI), and prostate imaging (PI) RADS

Newer structured reporting manners, the reporting and data system (RADS), have made vast steps in improving standardized and structured reporting, allowing better communication between radiologists and referring providers. This has been implemented in several fields: breast (BI-RADS), lung (Lung-RADS), liver (LI-RADS), thyroid (TI-RADS), prostate (PI-RADS), and in cardiovascular radiology (CAD-RADS). The field of nuclear cardiology began its efforts of standardization years ago; however, a widespread standardized reporting structure has not yet been adopted. Such an approach in nuclear cardiology, the nuclear cardiology reporting and data system (NCAD-RADS), will assist radiologists and treating clinicians in conveying and understanding reports and determining the appropriate next steps in management. By linking explicit findings to defined recommendations, patients will receive more consistent and appropriate care.

A review on radiomics and the future of theranostics for patient selection in precision medicine

The growing complexity and volume of clinical data and the associated decision-making processes in oncology promote the advent of precision medicine. Precision (or personalised) medicine describes preventive and/or treatment procedures that take individual patient variability into account when proscribing treatment, and has been hindered in the past by the strict requirements of accurate, robust, repeatable and preferably non-invasive biomarkers to stratify both the patient and the disease. In oncology, tumour subtypes are traditionally measured through repeated invasive biopsies, which are taxing for the patient and are cost and labour intensive. Quantitative analysis of routine clinical imaging provides an opportunity to capture tumour heterogeneity non-invasively, cost-effectively and on large scale. In current clinical practice radiological images are qualitatively analysed by expert radiologists whose interpretation is known to suffer from inter- and intra-operator variability. Radiomics, the high-throughput mining of image features from medical images, provides a quantitative and robust method to assess tumour heterogeneity, and radiomics-based signatures provide a powerful tool for precision medicine in cancer treatment. This study aims to provide an overview of the current state of radiomics as a precision medicine decision support tool. We first provide an overview of the requirements and challenges radiomics currently faces in being incorporated as a tool for precision medicine, followed by an outline of radiomics' current applications in the treatment of various types of cancer. We finish with a discussion of possible future advances that can further develop radiomics as a precision medicine tool.

Deep Learning in Neuroradiology

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.

Bridging the Gap: Identifying Global Trends in Gender Disparity Among the Radiology Physician Workforce

RATIONALE AND OBJECTIVES

Women make up half of American medical school graduates, but remain underrepresented among radiologists. This study sought to determine whether workforce gender disparities exist in other countries, and to identify any country-specific indices associated with increased female representation.

MATERIALS AND METHODS

In this cross-sectional study, 95 professional radiology organizations in 75 countries were contacted via email to provide membership statistics, including proportion of female members, female members aged 35 or under, and women in society leadership positions. Country-specific metrics collected included gross domestic product, Gini index, percent female medical school enrollment, and Gender Development Index for the purposes of univariate multiple regression analysis.

RESULTS

Twenty-nine organizations provided data on 184,888 radiologists, representing 26 countries from Europe (n = 12), North America (n = 2), Central/South America (n = 6), Oceania (n = 2), Asia (n = 3), and Africa (n = 1) for a response rate of 34.7% (26/75). Globally, 33.5% of radiologists are female. Women constitute a higher proportion of younger radiologists, with 48.5% of radiologists aged 35 or under being female. Female representation in radiology is lowest in the United States (27.2%), highest in Thailand (85.0%), and most variable in Europe (mean 40.1%, range 28.8%-68.9%). The proportion of female radiologists was positively associated with a country's Gender Development Index (P = .006), percent female medical student enrollment (P = .001), and Gini index (P = .002), and negatively associated with gross domestic product (P = .03).

CONCLUSIONS

Women are underrepresented in radiology globally, most notably in the United States. Countries with greater representation of women had higher gender equality and percent female medical school enrollment, suggesting these factors may play a role in the gender gap.

Quantifying Analysis of Uncertainty in Medical Reporting: Creation of User and Context-Specific Uncertainty Profiles

While uncertainty is ubiquitous in medical practice, minimal work to date has been performed to analyze the cause and effect relationship between uncertainty and patient outcomes. In medical imaging practice, uncertainty in the radiology report has been well documented to be a source of clinician dissatisfaction. Before one can effectively create intervention strategies aimed at reducing uncertainty, it must first be better understood through context- and user-specific analysis. One strategy for accomplishing this task is to characterize the source of uncertainty and create user-specific uncertainty profiles which take into account a number of provider-specific variables which may contribute to report uncertainty. The resulting data can in turn be used to create real-time report uncertainty metrics aimed at providing uncertainty analytics at the point of care, for the combined purposes of decision support, improved communication, and enhanced clinical/economic outcomes.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

Characteristics, Trends, and Quality of Systematic Review and Meta-Analysis in General Radiology between 2007 and 2015

RATIONALE AND OBJECTIVES

To evaluate the trends, characteristics, and quality of systematic review and meta-analysis in general radiology journals.

MATERIALS AND METHODS

We performed a PubMed search to identify systematic reviews and meta-analyses that had been carried out in the field of radiology between 2007 and 2015. The following data were extracted: journal, impact factor, type of research, year of publication, radiological subspecialty, imaging modalities used, number of authors, affiliated department of the first and corresponding authors, presence of a radiologist and a statistician among the authors, discordance between the first and corresponding authors, funding, country of first author, methodological quality, methods used for quality assessment, and statistics.

RESULTS

Ultimately, we included 210 articles from nine general radiology journals. The European Journal of Radiology was the most common journal represented (47 of 210; 22.4%). Meta-analyses (n = 177; 84.3%) were published about five times more than systematic reviews without meta-analysis (n = 33; 15.7%). Radiology of the gastrointestinal tract was the most commonly represented subspecialty (n = 49, 23.3%). The first authors were most frequently located in China (n = 64; 30.3%). In terms of modality, magnetic resonance imaging was used most often (n = 59; 28.1%). The number of authors tended to progressively increase over time, and the ratio of discordance between the first and corresponding authors also increased significantly, as did the proportion of research that has received funding from an external source. The mean AMSTAR assessment score improved over time (5.87/11 in 2007-2009, 7.11/11 in 2010-2012, and 7.49/11 in 2013-2015). In this regard, the journal Radiology had the highest score (7.59/11).

CONCLUSIONS

The quantity and quality of radiological meta-analyses have significantly increased over the past 9 years; however, specific weak areas remain, providing the opportunity for quality improvement.

"Radio-oncomics" : The potential of radiomics in radiation oncology

INTRODUCTION

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow.

METHODS

After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created.

RESULTS

Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization.

DISCUSSION

Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches.

CONCLUSION

This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Radiology Education in the Era of Population-based Medicine in the United States

Over the past several decades, the practice of radiology has undergone substantial change primarily related to advances in imaging technology, changes in the infrastructure of healthcare delivery, and evolution of reimbursement systems. Yet to a large extent, the educational system has not substantially changed. In this perspective, we discuss the need for radiology education to adapt and address these essential systems-based skills (business, quality, informatics, leadership, population-based medicine, and interprofessional teamwork) to ensure that future radiology graduates will thrive in this evolving healthcare environment.

Evolution of the Preliminary Clinical Year and the Case for a Categorical Diagnostic Radiology Residency

PURPOSE

While other specialties traditionally utilizing a segregated clinical internship year have slowly progressed toward integrated training curricula, diagnostic radiology has been slow to adopt this path. The aim of this study was to analyze the trends in stand-alone preliminary clinical years as well as the shift toward categorical residencies currently being undertaken in other specialties. Advantages of mimicking the trends of other specialties and current integrated radiology programs are discussed. The perception of diagnostic radiology as a competitive specialty is explored, and the prospect of change as a recruiting tool is examined.

METHODS

Data assimilated by the NRMP from 1994 through 2016 were processed and analyzed.

RESULTS

The total number of postgraduate year (PGY) 1 preliminary year programs has remained relatively constant over the past 10 years despite a gradual increase in overall NRMP applicants. The proportion of these programs offered as a transitional year declined from 31% in 1994 to 20% in 2016. The proportion of categorical anesthesiology positions gradually rose from 43% in 2007 to 70% in 2016. The fraction of categorical neurology positions increased from 30% in 2007 to 59% in 2016. The percentage of diagnostic radiology programs beginning at the PGY 1 level has been relatively constant at 12% to 14% since 2007. Dermatology has increased advanced (PGY 2) positions while decreasing categorical (PGY 1) positions. Those matching in diagnostic radiology have performed at a high level compared with the composite NRMP average since 2007. In the 2015 match, there were 65 diagnostic radiology programs that did not fill all of their offered positions. Of the institutions housing these programs, only 22% of them had preliminary internal medicine or transitional year positions available after the match.

CONCLUSIONS

In response to the evolving nature of health care and graduate medical education, other specialties are gradually shifting toward curricular structures that begin at the PGY 1 level. By considering such a transition, diagnostic radiology would be well served to position itself as a valuable clinical specialty while maintaining a lesser dependence on other specialties to train its physicians.

PET-CT in the UK: current status and future directions

Combined positron-emission tomography and computed tomography (PET-CT) has taken the oncological world by storm since being introduced into the clinical domain in the early 21(st) century and is firmly established in the management pathway of many different tumour types. Non-oncological applications of PET-CT represent a smaller but steadily growing area of interest. PET-CT continues to be the focus of a large number of research studies and keeping up-to-date with the literature is important but represents a challenge. Consequently guidelines recommending PET-CT usage need to be revised regularly to encompass new developments. The purpose of this article is twofold: first, it provides a detailed review of the evidence-base underpinning the major uses of PET-CT in clinical practice, which may be of value to a wide-range of individuals, including those directly involved with PET-CT and to a much larger group with limited exposure, but for whom a précis of the current state-of-play may help inform other radiology and multidisciplinary team (MDT) work; the second purpose is as a companion to revised guidelines on evidence-based indications for PET-CT in the UK (being published concurrently) providing a detailed commentary on new indications with a summary of emerging data supporting these additional clinical uses of the technique.

Value of Imaging Part I: Perspectives for the Academic Radiologist

With payers and policymakers increasingly scrutinizing the value of medical imaging, opportunities abound for radiologists and radiology health services researchers to meaningfully and rigorously demonstrate value. Part one of this two-part series on the value of imaging explores the concept of value in health care from the perspective of multiple stakeholders and discusses the opportunities and challenges for radiologists and health service researchers to demonstrate value. The current absence of meaningful national value metrics also presents an opportunity for radiologists to take the lead on the discussions of these metrics that may serve as the basis for future value-based payments. As both practitioners and investigators, radiologists should consider the perspectives of multiple stakeholders in all they do-interdisciplinary support and cooperation are essential to the success of value-focused imaging research and initiatives that improve patient outcomes. Radiology departments that align their cultures, infrastructures, and incentives to support these initiatives will greatly increase their chances of being successful in these endeavors.

[Trends for the Geographic Distribution of Radiological Resources in Hokkaido, Japan: Data Analysis Using Gini Coefficient and Herfindahl-Hirschman Index]

The aim of this study is to analyze the maldistribution and the trends in the geographic distribution of radiological resources in secondary medical areas of Hokkaido. The distribution was measured by combining the Gini coefficient (GC), which is an indicator of inequality of distribution, and the Herfindahl-Hirschman index (HHI), which is mainly used to assess market concentration. Data concerning the distribution of radiological resources, such as CT, MRI, radiotherapy facilities (RTF), radiological technologists (RT), and medical doctors were obtained from official publications. CT was more equally distributed, and RTF was more inequality than other radiological resources in 2014. Radiological resources excluded CT were higher degree of concentration than population distribution, and it showed that they were located relatively more intensively in urban areas than in rural areas. During the period 1999-2014, the GC for CT, MRI, RTF, and RT decreased, while the HHI increased. These trends indicated increased equality of distribution of CT, MRI, RTF, and RT and the concentration in urban areas. This study suggested that GC and HHI could be powerful indicators for allocation planning of medical resources with further analysis of the maldistribution of medical resources.

Changing practice to prevent contrast-induced nephropathy

Based on updated evidence, a radiology nurse systematically engaged a multidisciplinary staff in testing a protocol to prevent contrast-induced nephropathy related to computed tomography. In a quality improvement project, the protocol combined preprocedure oral hydration with postprocedure intravenous saline. This protocol safely improved kidney function, reduced postprocedure time, and decreased annual cost. By applying theory, being persistent, presenting sound evidence, and unifying the team, one concerned staff nurse profoundly affected patient care and policy in an entire medical center.

Ultrasound in Rheumatology: two decades of rapid development and evolving implementation

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Twenty years of radiobiology in clinical practice: the Italian contribution

AIMS AND BACKGROUND

To present the Italian state-of-the-art contribution to radiobiology of external beam radiotherapy, brachytherapy, and radionuclide radiotherapy.

METHODS AND STUDY DESIGN

A survey of the literature was carried out, using PubMed, by some independent researchers of the Italian group of radiobiology. Each paper was reviewed by researchers of centers not comprising its authors. The survey was limited to papers in English published over the last 20 years, written by Italian investigators or in Italian institutions, excluding review articles.

RESULTS

A total of 135 papers have been published in journals with an impact factor, with an increase in the number of published papers over time, for external beam radiotherapy rather than radionuclide radiotherapy. The quantity and quality of the papers researched constitutes a proof of the enduring interest in clinical radiobiology among Italian investigators.

CONCLUSIONS

The survey could be useful to individuate expert partners for an Italian network on clinical radiobiology, addressing future collaborative investigations.

Hospitalization for cancer among radiologists in Taiwan

BACKGROUND

Population aging and the incremental use of high-tech instruments increase the demand for radiological examinations and treatments in medical services. The exposure of radiologists and other medical workers to medical treatment radiation may thus be increased.

OBJECTIVES

The aim of the study was to explore the average number of cancer hospitalizations and use of hospitalization as cancer treatment for radiologists compared with that for family medicine physicians, as well as the trends in the annual average number of cancer hospitalizations among radiologists.

METHODS

Research data were obtained from the 2000-2010 Taiwan National Health Insurance Research Database. These samples collected for this study were unbalanced panel data.

RESULTS

The average number of cancer hospitalizations for radiologists from 2000 to 2010 ranged between 3.67 and 28.26‰. After controlling the effects of gender, age, hospital accreditation level and year using generalized estimating equations with a binomial distribution and logit link function, our study found that radiologists had non significant higher risk of cancer hospitalizations compared with family medicine physicians. However, the average number of cancer hospitalizations for radiologists showed an annual decline from 2000 to 2010.

CONCLUSIONS

Compared with family medicine physicians, radiologists had non significant higher risk of cancer hospitalizations. The data period examined in this study was only 11 years. Considering the numerous new radiological procedures currently in use in modern medical treatments, the health status of medical radiation workers should be continuously monitored in the future.